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Sensitivity Analysis of SAR Estimators

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Author Info

  • Liu, Shuangzhe

    (University of Canberra, Canberra, Australia)

  • Polasek, Wolfgang

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

  • Sellner, Richard

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

Abstract

Estimators of spatial autoregressive (SAR) models depend in a highly non-linear way on the spatial correlation parameter and least squares (LS) estimators cannot be computed in closed form. We first compare two simple LS estimators by distance and covariance properties and then we study the local sensitivity behavior of these estimators using matrix derivatives. These results allow us to calculate the Taylor approximation of the least squares estimator in the spatial autoregression (SAR) model up to the second order. Using Kantorovich inequalities, we compare the covariance structure of the two estimators and we derive efficiency comparisons by upper bounds. Finally, we demonstrate our approach by an example for GDP and employment in 239 European NUTS2 regions. We find a good approximation behavior of the SAR estimator, evaluated around the non-spatial LS estimators. These results can be used as a basis for diagnostic tools to explore the sensitivity of spatial estimators.

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File URL: http://www.ihs.ac.at/publications/eco/es-262.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 262.

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Length: 19 pages
Date of creation: Jan 2011
Date of revision:
Handle: RePEc:ihs:ihsesp:262

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Related research

Keywords: Spatial autoregressive models; least squares estimators; sensitivity analysis; Taylor Approximations; Kantorovich inequality;

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Cited by:
  1. Liu, Shuangzhe & Ma, Tiefeng & Polasek, Wolfgang, 2012. "Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study," Economics Series 294, Institute for Advanced Studies.

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